Towards Uncertainty Quantification for Electrode Bending Prediction in Stereotactic Neurosurgery

Implantation accuracy of electrodes during stereotactic neurosurgery is necessary to ensure safety and efficacy. However, electrodes deflect from planned trajectories. Although mechanical models and data-driven approaches have been proposed for trajectory prediction, they lack to report uncertainty of the predictions. We propose to use Monte Carlo (MC) dropout on neural networks to quantify uncertainty of predicted electrode local displacement. We compute image features of 23 stereoelectroencephalography cases (241 electrodes) and use them as inputs to a neural network to regress electrode local displacement. We use MC dropout with 200 stochastic passes to quantify uncertainty of predictions. To validate our approach, we define a baseline model without dropout and compare it to a stochastic model using 10-fold cross-validation. Given a starting planned trajectory, we predicted electrode bending using inferred local displacement at the tip via simulation. We found MC dropout performed better than a non-stochastic baseline model and provided confidence intervals along the predicted trajectory of electrodes. We believe this approach facilitates better decision making for electrode bending prediction in surgical planning.